Triple
T1768709
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shanghai Metro |
E38822
|
entity |
| Predicate | hasLine |
P35
|
FINISHED |
| Object | Maglev Line |
E191905
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Maglev Line | Statement: [Shanghai Metro, hasLine, Maglev Line]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maglev Line Context triple: [Shanghai Metro, hasLine, Maglev Line]
-
A.
Shanghai Maglev Train
chosen
The Shanghai Maglev Train is a high-speed magnetic levitation railway in Shanghai, China, renowned as one of the fastest commercial train services in the world.
-
B.
Alweg Monorail
The Alweg Monorail is an elevated monorail system in Seattle that became an iconic symbol of mid-20th-century futuristic transportation design.
-
C.
Mattapan High-Speed Line
The Mattapan High-Speed Line is a light rail transit line in Boston, Massachusetts, operating historic trolley cars between Ashmont and Mattapan stations as part of the MBTA system.
-
D.
AeroTrain
AeroTrain is an automated underground people mover system that transports passengers between terminals at Washington Dulles International Airport.
-
E.
Tokyo Monorail
Tokyo Monorail is an urban transit line in Tokyo that provides rapid rail service between central Tokyo and Haneda Airport.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a8862e61708190af97b9838cc3f5de |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa648d9f2c8190aca4884648a69eb0 |
completed | March 6, 2026, 5:22 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ada991564c81909ae00fdcb47f52af |
completed | March 8, 2026, 4:53 p.m. |
Created at: March 4, 2026, 7:31 p.m.